Abstract: The present disclosure relates to a system and method for detecting occupants, and more specifically, to a system and method for accurately detecting positions of occupants in a building using beacon signals.

Abstract: An embodiment of the present disclosure provides an arc risk management method comprising: pre-processing measurement values of currents flowing into an electric apparatus; estimating a level of arc energy in the electric apparatus by inputting the measurement values into one artificial intelligence network comprising a first layer including a dilated convolutional neural network and a second layer including a recurrent neural network; and indicating an arc risk to the electric apparatus in a quantitative way according to the level of arc energy.

Abstract: The present disclosure relates to a system and method for detecting occupants, and more specifically, to a system and method for accurately detecting positions of occupants in a building using beacon signals.

Abstract: An embodiment of the present disclosure provides an arc detection method, in which an apparatus detects arcs, comprising the steps of: obtaining time series data for measured values of an electric current flowing in a wire; calculating first statistical values indicating dispersion degrees with time of the measured values or dispersion degrees with time of variances of the measured values from the time series data; and determining that an arc occurs in the wire or that the possibility of arc occurrence in the wire is high in a case when at least one of the first statistical values is out of a predefined range.

Abstract: An embodiment of the present disclosure provides an arc detection method, in which an apparatus detects arcs, comprising the steps of: obtaining time series data for measured values of an electric current flowing in a wire; calculating first statistical values indicating dispersion degrees with time of the measured values or dispersion degrees with time of variances of the measured values from the time series data; and determining that an arc occurs in the wire or that the possibility of arc occurrence in the wire is high in a case when at least one of the first statistical values is out of a predefined range.

Abstract: An embodiment of the present disclosure provides an arc detection method, in which an apparatus detects arcs, comprising the steps of: obtaining time series data for measured values of an electric current flowing in a wire; calculating first statistical values indicating dispersion degrees with time of the measured values or dispersion degrees with time of variances of the measured values from the time series data; and determining that an arc occurs in the wire or that the possibility of arc occurrence in the wire is high in a case when at least one of the first statistical values is out of a predefined range.

Abstract: An embodiment of the present disclosure provides an arc detection method, in which an apparatus detects arcs, comprising the steps of: obtaining time series data for measured values of an electric current flowing in a wire; calculating first statistical values indicating dispersion degrees with time of the measured values or dispersion degrees with time of variances of the measured values from the time series data; and determining that an arc occurs in the wire or that the possibility of arc occurrence in the wire is high in a case when at least one of the first statistical values is out of a predefined range.

Abstract: The present disclosure relates to a multifunctional energy storage system in which one energy storage system is capable of performing a plurality of functions and an operating method thereof.